Species delimitation using marginal likelihoods in conjunction with Bayes
factors has some advantages over alternative approaches.
The flexibility of being able to compare non-nested models that contain
different numbers of species, or different species assignments, is one key
advantage.
The methods also integrate over gene trees, species trees, and other model
parameters, allowing the marginal likelihoods of delimitations to be compared
without conditioning on any parameters being known.
Marginal likelihoods also provide a natural way to rank competing models while
automatically accounting for model complexity [1]}.
Finally, it is unnecessary to assign prior probabilities to the alternative
species delimitation models being compared.
The marginal likelihood of a delimitation provides the factor by which the data
update our prior expectations, regardless of what that expectation is
(Equation REF ).
As multi-species coalescent models continue to advance, using the marginal
likelihoods of delimitations will continue to be a powerful approach to
learning about biodiversity.
